 Connection
Casey Greene to Machine Learning
This is a "connection" page, showing publications Casey Greene has written about Machine Learning.
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Connection Strength |
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3.056 |
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Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods. 2023 Jun; 20(6):803-814.
Score: 0.585
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Crawford J, Greene CS. Incorporating biological structure into machine learning models in biomedicine. Curr Opin Biotechnol. 2020 06; 63:126-134.
Score: 0.463
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Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, Sander C, Cherniack AD, Mina M, Ciriello G, Schultz N, Sanchez Y, Greene CS. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep. 2018 04 03; 23(1):172-180.e3.
Score: 0.409
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Beaulieu-Jones BK, Greene CS. Reproducibility of computational workflows is automated using continuous analysis. Nat Biotechnol. 2017 04; 35(4):342-346.
Score: 0.380
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Way GP, Allaway RJ, Bouley SJ, Fadul CE, Sanchez Y, Greene CS. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma. BMC Genomics. 2017 02 06; 18(1):127.
Score: 0.378
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Foltz SM, Greene CS, Taroni JN. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously. Commun Biol. 2023 02 25; 6(1):222.
Score: 0.144
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Heil BJ, Hoffman MM, Markowetz F, Lee SI, Greene CS, Hicks SC. Reproducibility standards for machine learning in the life sciences. Nat Methods. 2021 10; 18(10):1132-1135.
Score: 0.130
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Hippen AA, Greene CS. Expanding and Remixing the Metadata Landscape. Trends Cancer. 2021 04; 7(4):276-278.
Score: 0.123
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Taroni JN, Grayson PC, Hu Q, Eddy S, Kretzler M, Merkel PA, Greene CS. MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease. Cell Syst. 2019 05 22; 8(5):380-394.e4.
Score: 0.111
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Way GP, Greene CS. Bayesian deep learning for single-cell analysis. Nat Methods. 2018 12; 15(12):1009-1010.
Score: 0.107
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Zhang S, Heil BJ, Mao W, Chikina M, Greene CS, Heller EA. MousiPLIER: A Mouse Pathway-Level Information Extractor Model. eNeuro. 2024 Jun; 11(6).
Score: 0.039
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Hu Q, Greene CS, Heller EA. Specific histone modifications associate with alternative exon selection during mammalian development. Nucleic Acids Res. 2020 05 21; 48(9):4709-4724.
Score: 0.030
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Banerjee J, Allaway RJ, Taroni JN, Baker A, Zhang X, Moon CI, Pratilas CA, Blakeley JO, Guinney J, Hirbe A, Greene CS, Gosline SJ. Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1. Genes (Basel). 2020 02 21; 11(2).
Score: 0.029
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Lin YT, Way GP, Barwick BG, Mariano MC, Marcoulis M, Ferguson ID, Driessen C, Boise LH, Greene CS, Wiita AP. Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma. Blood Adv. 2019 11 12; 3(21):3214-3227.
Score: 0.029
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Weissman GE, Hubbard RA, Ungar LH, Harhay MO, Greene CS, Himes BE, Halpern SD. Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay. Crit Care Med. 2018 07; 46(7):1125-1132.
Score: 0.026
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Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS, Liu Y, Akbani R, Feng B, Donehower LA, Miller C, Shen Y, Karimi M, Chen H, Kim P, Jia P, Shinbrot E, Zhang S, Liu J, Hu H, Bailey MH, Yau C, Wolf D, Zhao Z, Weinstein JN, Li L, Ding L, Mills GB, Laird PW, Wheeler DA, Shmulevich I, Monnat RJ, Xiao Y, Wang C. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep. 2018 04 03; 23(1):239-254.e6.
Score: 0.026
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Kacsoh BZ, Greene CS, Bosco G. Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning. G3 (Bethesda). 2017 11 06; 7(11):3705-3718.
Score: 0.025
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Yao X, Yan J, Liu K, Kim S, Nho K, Risacher SL, Greene CS, Moore JH, Saykin AJ, Shen L. Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. Bioinformatics. 2017 Oct 15; 33(20):3250-3257.
Score: 0.025
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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